Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations15533
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.6 MiB
Average record size in memory578.9 B

Variable types

Numeric9
Categorical5
Boolean4

Alerts

Gender is highly overall correlated with Height and 2 other fieldsHigh correlation
Height is highly overall correlated with GenderHigh correlation
Weight is highly overall correlated with Gender and 2 other fieldsHigh correlation
WeightCategory is highly overall correlated with Gender and 2 other fieldsHigh correlation
family_history_with_overweight is highly overall correlated with Weight and 1 other fieldsHigh correlation
FAVC is highly imbalanced (57.4%) Imbalance
CAEC is highly imbalanced (61.2%) Imbalance
SMOKE is highly imbalanced (91.0%) Imbalance
SCC is highly imbalanced (79.0%) Imbalance
MTRANS is highly imbalanced (63.7%) Imbalance
id is uniformly distributed Uniform
id has unique values Unique
FAF has 3799 (24.5%) zeros Zeros
TUE has 4966 (32.0%) zeros Zeros

Reproduction

Analysis started2025-10-23 08:56:39.115392
Analysis finished2025-10-23 08:56:49.470211
Duration10.35 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

Uniform  Unique 

Distinct15533
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7766
Minimum0
Maximum15532
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size121.5 KiB
2025-10-23T14:26:49.564106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile776.6
Q13883
median7766
Q311649
95-th percentile14755.4
Maximum15532
Range15532
Interquartile range (IQR)7766

Descriptive statistics

Standard deviation4484.1352
Coefficient of variation (CV)0.57740603
Kurtosis-1.2
Mean7766
Median Absolute Deviation (MAD)3883
Skewness0
Sum1.2062928 × 108
Variance20107468
MonotonicityStrictly increasing
2025-10-23T14:26:49.699341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15532 1
 
< 0.1%
0 1
 
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
Other values (15523) 15523
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
15532 1
< 0.1%
15531 1
< 0.1%
15530 1
< 0.1%
15529 1
< 0.1%
15528 1
< 0.1%
15527 1
< 0.1%
15526 1
< 0.1%
15525 1
< 0.1%
15524 1
< 0.1%
15523 1
< 0.1%

Gender
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size819.2 KiB
Male
7783 
Female
7750 

Length

Max length6
Median length4
Mean length4.9978755
Min length4

Characters and Unicode

Total characters77632
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 7783
50.1%
Female 7750
49.9%

Length

2025-10-23T14:26:49.816767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-23T14:26:49.884494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male 7783
50.1%
female 7750
49.9%

Most occurring characters

ValueCountFrequency (%)
e 23283
30.0%
a 15533
20.0%
l 15533
20.0%
M 7783
 
10.0%
F 7750
 
10.0%
m 7750
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 62099
80.0%
Uppercase Letter 15533
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 23283
37.5%
a 15533
25.0%
l 15533
25.0%
m 7750
 
12.5%
Uppercase Letter
ValueCountFrequency (%)
M 7783
50.1%
F 7750
49.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 77632
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 23283
30.0%
a 15533
20.0%
l 15533
20.0%
M 7783
 
10.0%
F 7750
 
10.0%
m 7750
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77632
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 23283
30.0%
a 15533
20.0%
l 15533
20.0%
M 7783
 
10.0%
F 7750
 
10.0%
m 7750
 
10.0%

Age
Real number (ℝ)

Distinct1602
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.816308
Minimum14
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size121.5 KiB
2025-10-23T14:26:49.974444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile17.971786
Q120
median22.771612
Q326
95-th percentile35.322112
Maximum61
Range47
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.663167
Coefficient of variation (CV)0.23778526
Kurtosis3.5963108
Mean23.816308
Median Absolute Deviation (MAD)3.228388
Skewness1.5719763
Sum369938.71
Variance32.07146
MonotonicityNot monotonic
2025-10-23T14:26:50.090932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 1438
 
9.3%
26 1337
 
8.6%
21 1252
 
8.1%
23 900
 
5.8%
19 674
 
4.3%
20 395
 
2.5%
17 394
 
2.5%
22 381
 
2.5%
33 156
 
1.0%
27 130
 
0.8%
Other values (1592) 8476
54.6%
ValueCountFrequency (%)
14 3
 
< 0.1%
15 2
 
< 0.1%
16 75
0.5%
16.093234 3
 
< 0.1%
16.120699 1
 
< 0.1%
16.129279 5
 
< 0.1%
16.140751 1
 
< 0.1%
16.172992 3
 
< 0.1%
16.178483 1
 
< 0.1%
16.198153 2
 
< 0.1%
ValueCountFrequency (%)
61 2
 
< 0.1%
56 1
 
< 0.1%
55.493687 1
 
< 0.1%
55.272573 1
 
< 0.1%
55.24625 1
 
< 0.1%
55.137881 2
 
< 0.1%
55.022494 8
 
0.1%
55 27
0.2%
52 2
 
< 0.1%
51 1
 
< 0.1%

Height
Real number (ℝ)

High correlation 

Distinct1723
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6999182
Minimum1.45
Maximum1.975663
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size121.5 KiB
2025-10-23T14:26:50.208164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.45
5-th percentile1.556211
Q11.630927
median1.7
Q31.762921
95-th percentile1.8457554
Maximum1.975663
Range0.525663
Interquartile range (IQR)0.131994

Descriptive statistics

Standard deviation0.087670026
Coefficient of variation (CV)0.051573086
Kurtosis-0.56183456
Mean1.6999182
Median Absolute Deviation (MAD)0.066509
Skewness0.01000144
Sum26404.829
Variance0.0076860335
MonotonicityNot monotonic
2025-10-23T14:26:50.348274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.7 1018
 
6.6%
1.65 570
 
3.7%
1.75 504
 
3.2%
1.6 491
 
3.2%
1.8 394
 
2.5%
1.62 303
 
2.0%
1.72 230
 
1.5%
1.56 190
 
1.2%
1.63 186
 
1.2%
1.55 164
 
1.1%
Other values (1713) 11483
73.9%
ValueCountFrequency (%)
1.45 2
 
< 0.1%
1.456346 2
 
< 0.1%
1.48 8
 
0.1%
1.481682 1
 
< 0.1%
1.483284 1
 
< 0.1%
1.486484 3
 
< 0.1%
1.489409 1
 
< 0.1%
1.498561 3
 
< 0.1%
1.5 131
0.8%
1.502609 2
 
< 0.1%
ValueCountFrequency (%)
1.975663 4
 
< 0.1%
1.947406 3
 
< 0.1%
1.942725 3
 
< 0.1%
1.931263 2
 
< 0.1%
1.931242 1
 
< 0.1%
1.930416 1
 
< 0.1%
1.93 11
0.1%
1.92 1
 
< 0.1%
1.919557 2
 
< 0.1%
1.919543 2
 
< 0.1%

Weight
Real number (ℝ)

High correlation 

Distinct1836
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.785225
Minimum39
Maximum165.05727
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size121.5 KiB
2025-10-23T14:26:50.478319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile49
Q166
median84
Q3111.60055
95-th percentile132.11649
Maximum165.05727
Range126.05727
Interquartile range (IQR)45.600553

Descriptive statistics

Standard deviation26.369144
Coefficient of variation (CV)0.30038249
Kurtosis-0.98657917
Mean87.785225
Median Absolute Deviation (MAD)22.735215
Skewness0.10855155
Sum1363567.9
Variance695.33177
MonotonicityNot monotonic
2025-10-23T14:26:50.609942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 644
 
4.1%
75 494
 
3.2%
50 450
 
2.9%
60 377
 
2.4%
70 376
 
2.4%
45 242
 
1.6%
65 229
 
1.5%
78 225
 
1.4%
85 223
 
1.4%
42 213
 
1.4%
Other values (1826) 12060
77.6%
ValueCountFrequency (%)
39 1
 
< 0.1%
39.101805 3
< 0.1%
39.12631 1
 
< 0.1%
39.371523 2
< 0.1%
39.535047 1
 
< 0.1%
39.581159 1
 
< 0.1%
39.695295 3
< 0.1%
39.850137 1
 
< 0.1%
40 2
< 0.1%
40.202773 3
< 0.1%
ValueCountFrequency (%)
165.057269 3
 
< 0.1%
160.935351 11
0.1%
160.639405 3
 
< 0.1%
155.872093 2
 
< 0.1%
155.242672 2
 
< 0.1%
154.618446 3
 
< 0.1%
153.959945 2
 
< 0.1%
153.149491 6
< 0.1%
152.720545 4
 
< 0.1%
152.567671 5
< 0.1%

family_history_with_overweight
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
True
12696 
False
2837 
ValueCountFrequency (%)
True 12696
81.7%
False 2837
 
18.3%
2025-10-23T14:26:50.692186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

FAVC
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
True
14184 
False
 
1349
ValueCountFrequency (%)
True 14184
91.3%
False 1349
 
8.7%
2025-10-23T14:26:50.734568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

FCVC
Real number (ℝ)

Distinct872
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4429168
Minimum1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size121.5 KiB
2025-10-23T14:26:50.821034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.850496
Q12
median2.34222
Q33
95-th percentile3
Maximum3
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.53089467
Coefficient of variation (CV)0.21732
Kurtosis-0.91646326
Mean2.4429168
Median Absolute Deviation (MAD)0.405741
Skewness-0.33204298
Sum37945.826
Variance0.28184915
MonotonicityNot monotonic
2025-10-23T14:26:50.949834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 5816
37.4%
3 5669
36.5%
1 199
 
1.3%
2.9673 92
 
0.6%
2.766612 39
 
0.3%
2.938616 37
 
0.2%
2.9553 31
 
0.2%
2.225149 24
 
0.2%
2.57649 23
 
0.1%
2.819934 23
 
0.1%
Other values (862) 3580
23.0%
ValueCountFrequency (%)
1 199
1.3%
1.002564 1
 
< 0.1%
1.003566 2
 
< 0.1%
1.005578 12
 
0.1%
1.006436 1
 
< 0.1%
1.00876 5
 
< 0.1%
1.021136 1
 
< 0.1%
1.031149 11
 
0.1%
1.036159 6
 
< 0.1%
1.036414 4
 
< 0.1%
ValueCountFrequency (%)
3 5669
36.5%
2.998441 2
 
< 0.1%
2.997951 8
 
0.1%
2.997524 4
 
< 0.1%
2.996717 8
 
0.1%
2.996186 5
 
< 0.1%
2.995599 3
 
< 0.1%
2.99448 1
 
< 0.1%
2.993634 1
 
< 0.1%
2.992606 1
 
< 0.1%

NCP
Real number (ℝ)

Distinct645
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7604249
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size121.5 KiB
2025-10-23T14:26:51.075056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median3
Q33
95-th percentile3.489918
Maximum4
Range3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.70646285
Coefficient of variation (CV)0.2559254
Kurtosis1.8251762
Mean2.7604249
Median Absolute Deviation (MAD)0
Skewness-1.5612644
Sum42877.68
Variance0.49908976
MonotonicityNot monotonic
2025-10-23T14:26:51.195640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 10985
70.7%
1 1493
 
9.6%
4 536
 
3.5%
2.993623 30
 
0.2%
2.695396 19
 
0.1%
1.894384 18
 
0.1%
2.658837 17
 
0.1%
2.993634 16
 
0.1%
2.962004 16
 
0.1%
2.977909 16
 
0.1%
Other values (635) 2387
 
15.4%
ValueCountFrequency (%)
1 1493
9.6%
1.000283 3
 
< 0.1%
1.000414 2
 
< 0.1%
1.00061 5
 
< 0.1%
1.001383 2
 
< 0.1%
1.001542 7
 
< 0.1%
1.001633 7
 
< 0.1%
1.009426 4
 
< 0.1%
1.010319 6
 
< 0.1%
1.014916 2
 
< 0.1%
ValueCountFrequency (%)
4 536
3.5%
3.998766 2
 
< 0.1%
3.998618 4
 
< 0.1%
3.995957 2
 
< 0.1%
3.995147 3
 
< 0.1%
3.994588 3
 
< 0.1%
3.990925 2
 
< 0.1%
3.98955 4
 
< 0.1%
3.989492 1
 
< 0.1%
3.987707 7
 
< 0.1%

CAEC
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size879.3 KiB
Sometimes
13126 
Frequently
1858 
Always
 
346
no
 
203

Length

Max length10
Median length9
Mean length8.9613082
Min length2

Characters and Unicode

Total characters139196
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSometimes
2nd rowFrequently
3rd rowSometimes
4th rowSometimes
5th rowSometimes

Common Values

ValueCountFrequency (%)
Sometimes 13126
84.5%
Frequently 1858
 
12.0%
Always 346
 
2.2%
no 203
 
1.3%

Length

2025-10-23T14:26:51.314070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-23T14:26:51.385457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sometimes 13126
84.5%
frequently 1858
 
12.0%
always 346
 
2.2%
no 203
 
1.3%

Most occurring characters

ValueCountFrequency (%)
e 29968
21.5%
m 26252
18.9%
t 14984
10.8%
s 13472
9.7%
o 13329
9.6%
S 13126
9.4%
i 13126
9.4%
y 2204
 
1.6%
l 2204
 
1.6%
n 2061
 
1.5%
Other values (7) 8470
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 123866
89.0%
Uppercase Letter 15330
 
11.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 29968
24.2%
m 26252
21.2%
t 14984
12.1%
s 13472
10.9%
o 13329
10.8%
i 13126
10.6%
y 2204
 
1.8%
l 2204
 
1.8%
n 2061
 
1.7%
r 1858
 
1.5%
Other values (4) 4408
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
S 13126
85.6%
F 1858
 
12.1%
A 346
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 139196
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 29968
21.5%
m 26252
18.9%
t 14984
10.8%
s 13472
9.7%
o 13329
9.6%
S 13126
9.4%
i 13126
9.4%
y 2204
 
1.6%
l 2204
 
1.6%
n 2061
 
1.5%
Other values (7) 8470
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 139196
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 29968
21.5%
m 26252
18.9%
t 14984
10.8%
s 13472
9.7%
o 13329
9.6%
S 13126
9.4%
i 13126
9.4%
y 2204
 
1.6%
l 2204
 
1.6%
n 2061
 
1.5%
Other values (7) 8470
 
6.1%

SMOKE
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
False
15356 
True
 
177
ValueCountFrequency (%)
False 15356
98.9%
True 177
 
1.1%
2025-10-23T14:26:51.439063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

CH2O
Real number (ℝ)

Distinct1408
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0276261
Minimum1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size121.5 KiB
2025-10-23T14:26:51.520212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.796257
median2
Q32.531456
95-th percentile3
Maximum3
Range2
Interquartile range (IQR)0.735199

Descriptive statistics

Standard deviation0.60773268
Coefficient of variation (CV)0.29972621
Kurtosis-0.73670285
Mean2.0276261
Median Absolute Deviation (MAD)0.39886
Skewness-0.20927674
Sum31495.116
Variance0.36933901
MonotonicityNot monotonic
2025-10-23T14:26:51.644906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 4992
32.1%
1 2117
 
13.6%
3 1172
 
7.5%
2.868167 50
 
0.3%
2.825629 48
 
0.3%
2.619517 45
 
0.3%
2.72005 42
 
0.3%
2.625537 40
 
0.3%
2.770732 38
 
0.2%
2.613928 33
 
0.2%
Other values (1398) 6956
44.8%
ValueCountFrequency (%)
1 2117
13.6%
1.000463 2
 
< 0.1%
1.000536 4
 
< 0.1%
1.000544 4
 
< 0.1%
1.000695 2
 
< 0.1%
1.001208 1
 
< 0.1%
1.001995 1
 
< 0.1%
1.002292 6
 
< 0.1%
1.003063 2
 
< 0.1%
1.003531 1
 
< 0.1%
ValueCountFrequency (%)
3 1172
7.5%
2.999495 3
 
< 0.1%
2.99675 1
 
< 0.1%
2.991671 2
 
< 0.1%
2.989389 3
 
< 0.1%
2.988771 2
 
< 0.1%
2.987718 6
 
< 0.1%
2.987406 2
 
< 0.1%
2.984323 3
 
< 0.1%
2.984153 6
 
< 0.1%

SCC
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
False
15019 
True
 
514
ValueCountFrequency (%)
False 15019
96.7%
True 514
 
3.3%
2025-10-23T14:26:51.717906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

FAF
Real number (ℝ)

Zeros 

Distinct1274
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9769677
Minimum0
Maximum3
Zeros3799
Zeros (%)24.5%
Negative0
Negative (%)0.0%
Memory size121.5 KiB
2025-10-23T14:26:51.793020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.00705
median1
Q31.582675
95-th percentile2.491946
Maximum3
Range3
Interquartile range (IQR)1.575625

Descriptive statistics

Standard deviation0.83684097
Coefficient of variation (CV)0.85656974
Kurtosis-0.47443387
Mean0.9769677
Median Absolute Deviation (MAD)0.869583
Skewness0.51452887
Sum15175.239
Variance0.7003028
MonotonicityNot monotonic
2025-10-23T14:26:52.132700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3799
24.5%
1 3103
20.0%
2 1737
 
11.2%
3 603
 
3.9%
0.01586 37
 
0.2%
1.097905 35
 
0.2%
1.68249 33
 
0.2%
1.427037 33
 
0.2%
1.866839 27
 
0.2%
0.926201 24
 
0.2%
Other values (1264) 6102
39.3%
ValueCountFrequency (%)
0 3799
24.5%
9.6 × 10-57
 
< 0.1%
0.000272 8
 
0.1%
0.000454 7
 
< 0.1%
0.001015 8
 
0.1%
0.001086 7
 
< 0.1%
0.001272 5
 
< 0.1%
0.001297 18
 
0.1%
0.00203 4
 
< 0.1%
0.00342 7
 
< 0.1%
ValueCountFrequency (%)
3 603
3.9%
2.999918 2
 
< 0.1%
2.977543 1
 
< 0.1%
2.971832 2
 
< 0.1%
2.936551 4
 
< 0.1%
2.931527 3
 
< 0.1%
2.892922 17
 
0.1%
2.891986 7
 
< 0.1%
2.89118 8
 
0.1%
2.881838 1
 
< 0.1%

TUE
Real number (ℝ)

Zeros 

Distinct1207
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.61381329
Minimum0
Maximum2
Zeros4966
Zeros (%)32.0%
Negative0
Negative (%)0.0%
Memory size121.5 KiB
2025-10-23T14:26:52.240504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.566353
Q31
95-th percentile2
Maximum2
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.60222272
Coefficient of variation (CV)0.9811171
Kurtosis-0.41502667
Mean0.61381329
Median Absolute Deviation (MAD)0.460458
Skewness0.67442988
Sum9534.3619
Variance0.3626722
MonotonicityNot monotonic
2025-10-23T14:26:52.356677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4966
32.0%
1 3255
21.0%
2 844
 
5.4%
0.0026 60
 
0.4%
0.723154 46
 
0.3%
0.088236 43
 
0.3%
0.630866 37
 
0.2%
0.15171 37
 
0.2%
0.62535 31
 
0.2%
0.200379 30
 
0.2%
Other values (1197) 6184
39.8%
ValueCountFrequency (%)
0 4966
32.0%
7.3 × 10-52
 
< 0.1%
0.000355 1
 
< 0.1%
0.000436 3
 
< 0.1%
0.001096 5
 
< 0.1%
0.00133 10
 
0.1%
0.001337 6
 
< 0.1%
0.00135 1
 
< 0.1%
0.001518 9
 
0.1%
0.00159 9
 
0.1%
ValueCountFrequency (%)
2 844
5.4%
1.99219 2
 
< 0.1%
1.990925 1
 
< 0.1%
1.990617 3
 
< 0.1%
1.983678 1
 
< 0.1%
1.980875 5
 
< 0.1%
1.978043 7
 
< 0.1%
1.972926 2
 
< 0.1%
1.97117 10
 
0.1%
1.969507 5
 
< 0.1%

CALC
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size854.1 KiB
Sometimes
11285 
no
3841 
Frequently
 
407

Length

Max length10
Median length9
Mean length7.2952424
Min length2

Characters and Unicode

Total characters113317
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSometimes
2nd rowno
3rd rowno
4th rowSometimes
5th rowSometimes

Common Values

ValueCountFrequency (%)
Sometimes 11285
72.7%
no 3841
 
24.7%
Frequently 407
 
2.6%

Length

2025-10-23T14:26:52.466264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-23T14:26:52.527207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sometimes 11285
72.7%
no 3841
 
24.7%
frequently 407
 
2.6%

Most occurring characters

ValueCountFrequency (%)
e 23384
20.6%
m 22570
19.9%
o 15126
13.3%
t 11692
10.3%
S 11285
10.0%
i 11285
10.0%
s 11285
10.0%
n 4248
 
3.7%
F 407
 
0.4%
r 407
 
0.4%
Other values (4) 1628
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 101625
89.7%
Uppercase Letter 11692
 
10.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 23384
23.0%
m 22570
22.2%
o 15126
14.9%
t 11692
11.5%
i 11285
11.1%
s 11285
11.1%
n 4248
 
4.2%
r 407
 
0.4%
q 407
 
0.4%
u 407
 
0.4%
Other values (2) 814
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
S 11285
96.5%
F 407
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 113317
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 23384
20.6%
m 22570
19.9%
o 15126
13.3%
t 11692
10.3%
S 11285
10.0%
i 11285
10.0%
s 11285
10.0%
n 4248
 
3.7%
F 407
 
0.4%
r 407
 
0.4%
Other values (4) 1628
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 113317
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 23384
20.6%
m 22570
19.9%
o 15126
13.3%
t 11692
10.3%
S 11285
10.0%
i 11285
10.0%
s 11285
10.0%
n 4248
 
3.7%
F 407
 
0.4%
r 407
 
0.4%
Other values (4) 1628
 
1.4%

MTRANS
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
Public_Transportation
12470 
Automobile
2669 
Walking
 
340
Motorbike
 
30
Bike
 
24

Length

Max length21
Median length21
Mean length18.754008
Min length4

Characters and Unicode

Total characters291306
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPublic_Transportation
2nd rowAutomobile
3rd rowPublic_Transportation
4th rowPublic_Transportation
5th rowPublic_Transportation

Common Values

ValueCountFrequency (%)
Public_Transportation 12470
80.3%
Automobile 2669
 
17.2%
Walking 340
 
2.2%
Motorbike 30
 
0.2%
Bike 24
 
0.2%

Length

2025-10-23T14:26:52.606292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-23T14:26:52.673629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
public_transportation 12470
80.3%
automobile 2669
 
17.2%
walking 340
 
2.2%
motorbike 30
 
0.2%
bike 24
 
0.2%

Most occurring characters

ValueCountFrequency (%)
o 30338
10.4%
i 28003
 
9.6%
t 27639
 
9.5%
a 25280
 
8.7%
n 25280
 
8.7%
r 24970
 
8.6%
l 15479
 
5.3%
b 15169
 
5.2%
u 15139
 
5.2%
P 12470
 
4.3%
Other values (13) 71539
24.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 250833
86.1%
Uppercase Letter 28003
 
9.6%
Connector Punctuation 12470
 
4.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 30338
12.1%
i 28003
11.2%
t 27639
11.0%
a 25280
10.1%
n 25280
10.1%
r 24970
10.0%
l 15479
6.2%
b 15169
6.0%
u 15139
6.0%
c 12470
5.0%
Other values (6) 31066
12.4%
Uppercase Letter
ValueCountFrequency (%)
P 12470
44.5%
T 12470
44.5%
A 2669
 
9.5%
W 340
 
1.2%
M 30
 
0.1%
B 24
 
0.1%
Connector Punctuation
ValueCountFrequency (%)
_ 12470
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 278836
95.7%
Common 12470
 
4.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 30338
10.9%
i 28003
10.0%
t 27639
9.9%
a 25280
9.1%
n 25280
9.1%
r 24970
9.0%
l 15479
 
5.6%
b 15169
 
5.4%
u 15139
 
5.4%
P 12470
 
4.5%
Other values (12) 59069
21.2%
Common
ValueCountFrequency (%)
_ 12470
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 291306
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 30338
10.4%
i 28003
 
9.6%
t 27639
 
9.5%
a 25280
 
8.7%
n 25280
 
8.7%
r 24970
 
8.6%
l 15479
 
5.3%
b 15169
 
5.2%
u 15139
 
5.2%
P 12470
 
4.3%
Other values (13) 71539
24.6%

WeightCategory
Categorical

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size987.2 KiB
Obesity_Type_III
2983 
Obesity_Type_II
2403 
Normal_Weight
2345 
Obesity_Type_I
2207 
Overweight_Level_II
1881 
Other values (2)
3714 

Length

Max length19
Median length16
Mean length16.070109
Min length13

Characters and Unicode

Total characters249617
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOverweight_Level_II
2nd rowNormal_Weight
3rd rowInsufficient_Weight
4th rowObesity_Type_III
5th rowOverweight_Level_II

Common Values

ValueCountFrequency (%)
Obesity_Type_III 2983
19.2%
Obesity_Type_II 2403
15.5%
Normal_Weight 2345
15.1%
Obesity_Type_I 2207
14.2%
Overweight_Level_II 1881
12.1%
Insufficient_Weight 1870
12.0%
Overweight_Level_I 1844
11.9%

Length

2025-10-23T14:26:52.759982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-23T14:26:52.840065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
obesity_type_iii 2983
19.2%
obesity_type_ii 2403
15.5%
normal_weight 2345
15.1%
obesity_type_i 2207
14.2%
overweight_level_ii 1881
12.1%
insufficient_weight 1870
12.0%
overweight_level_i 1844
11.9%

Most occurring characters

ValueCountFrequency (%)
e 36171
14.5%
_ 26851
 
10.8%
I 23438
 
9.4%
i 19273
 
7.7%
t 17403
 
7.0%
y 15186
 
6.1%
O 11318
 
4.5%
s 9463
 
3.8%
g 7940
 
3.2%
h 7940
 
3.2%
Other values (17) 74634
29.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 170132
68.2%
Uppercase Letter 52634
 
21.1%
Connector Punctuation 26851
 
10.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 36171
21.3%
i 19273
11.3%
t 17403
10.2%
y 15186
8.9%
s 9463
 
5.6%
g 7940
 
4.7%
h 7940
 
4.7%
b 7593
 
4.5%
p 7593
 
4.5%
v 7450
 
4.4%
Other values (10) 34120
20.1%
Uppercase Letter
ValueCountFrequency (%)
I 23438
44.5%
O 11318
21.5%
T 7593
 
14.4%
W 4215
 
8.0%
L 3725
 
7.1%
N 2345
 
4.5%
Connector Punctuation
ValueCountFrequency (%)
_ 26851
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 222766
89.2%
Common 26851
 
10.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 36171
16.2%
I 23438
 
10.5%
i 19273
 
8.7%
t 17403
 
7.8%
y 15186
 
6.8%
O 11318
 
5.1%
s 9463
 
4.2%
g 7940
 
3.6%
h 7940
 
3.6%
b 7593
 
3.4%
Other values (16) 67041
30.1%
Common
ValueCountFrequency (%)
_ 26851
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 249617
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 36171
14.5%
_ 26851
 
10.8%
I 23438
 
9.4%
i 19273
 
7.7%
t 17403
 
7.0%
y 15186
 
6.1%
O 11318
 
4.5%
s 9463
 
3.8%
g 7940
 
3.2%
h 7940
 
3.2%
Other values (17) 74634
29.9%

Interactions

2025-10-23T14:26:48.211011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:40.859586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:41.720266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:42.673531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:43.498988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:44.360835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:45.320832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:46.188118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:47.106644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:48.319229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:40.960497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:41.813815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:42.773456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:43.597963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:44.469302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:45.423877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:46.292219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:47.205992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:48.424718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:41.049618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:41.891173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:42.861890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:43.680981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:44.568884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:45.513910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:46.386413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:47.302926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:48.530035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:41.140534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:41.977201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:42.946957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:43.772376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:44.678676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:45.605809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:46.486542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:47.394319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:48.635722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:41.235113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:42.061140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:43.037267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:43.860996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:44.780212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:45.700724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:46.588323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:47.487291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:48.744041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:41.333251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:42.306520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:43.135661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:43.957007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:44.885238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:45.801209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:46.691866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:47.586312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:48.850055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:41.425646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:42.392540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:43.230365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:44.047007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:44.987357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:45.891666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:46.793059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:47.674836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:48.964575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:41.530115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:42.489453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:43.322787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:44.151173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:45.090768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:45.992013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:46.900250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:47.774931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:49.055679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:41.621977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:42.576082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:43.408907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:44.251429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:45.201864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:46.085680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:47.000028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T14:26:47.869177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-23T14:26:52.949758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeCAECCALCCH2OFAFFAVCFCVCGenderHeightMTRANSNCPSCCSMOKETUEWeightWeightCategoryfamily_history_with_overweightid
Age1.0000.1540.1920.086-0.2740.1220.0960.2540.0160.363-0.1210.1150.142-0.3020.4380.3470.3010.007
CAEC0.1541.0000.0950.1530.1160.1380.0920.0720.1400.0650.1510.1270.0160.1220.3120.3310.3380.000
CALC0.1920.0951.0000.1800.1470.1150.1630.0800.1250.0710.1340.0000.0140.1630.2920.3090.0130.000
CH2O0.0860.1530.1801.0000.0590.1660.1100.3310.1890.1010.0770.0770.051-0.0040.3450.3100.2770.008
FAF-0.2740.1160.1470.0591.0000.135-0.0860.3440.3210.1010.1180.0790.042-0.017-0.0620.2570.1830.017
FAVC0.1220.1380.1150.1660.1351.0000.0940.0200.1520.1240.0520.1120.0130.1380.2370.2740.1510.016
FCVC0.0960.0920.1630.110-0.0860.0941.0000.402-0.1080.0970.1290.0410.049-0.1340.2270.3250.132-0.009
Gender0.2540.0720.0800.3310.3440.0200.4021.0000.6380.1600.1570.0580.0570.2120.5230.6160.0930.000
Height0.0160.1400.1250.1890.3210.152-0.1080.6381.0000.0840.1120.1470.1080.0860.4240.2670.2980.013
MTRANS0.3630.0650.0710.1010.1010.1240.0970.1600.0841.0000.0530.0580.0300.1270.1530.1630.1300.000
NCP-0.1210.1510.1340.0770.1180.0520.1290.1570.1120.0531.0000.0700.0060.128-0.0220.2180.226-0.005
SCC0.1150.1270.0000.0770.0790.1120.0410.0580.1470.0580.0701.0000.0170.0670.2060.2170.1620.000
SMOKE0.1420.0160.0140.0510.0420.0130.0490.0570.1080.0300.0060.0171.0000.0310.0730.1000.0170.017
TUE-0.3020.1220.163-0.004-0.0170.138-0.1340.2120.0860.1270.1280.0670.0311.000-0.0620.2480.2010.001
Weight0.4380.3120.2920.345-0.0620.2370.2270.5230.4240.153-0.0220.2060.073-0.0621.0000.6470.5890.014
WeightCategory0.3470.3310.3090.3100.2570.2740.3250.6160.2670.1630.2180.2170.1000.2480.6471.0000.5590.008
family_history_with_overweight0.3010.3380.0130.2770.1830.1510.1320.0930.2980.1300.2260.1620.0170.2010.5890.5591.0000.000
id0.0070.0000.0000.0080.0170.016-0.0090.0000.0130.000-0.0050.0000.0170.0010.0140.0080.0001.000

Missing values

2025-10-23T14:26:49.215543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-23T14:26:49.363508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idGenderAgeHeightWeightfamily_history_with_overweightFAVCFCVCNCPCAECSMOKECH2OSCCFAFTUECALCMTRANSWeightCategory
00Male24.4430111.69999881.669950yesyes2.0000002.983297Sometimesno2.763573no0.0000000.976473SometimesPublic_TransportationOverweight_Level_II
11Female18.0000001.56000057.000000yesyes2.0000003.000000Frequentlyno2.000000no1.0000001.000000noAutomobileNormal_Weight
22Female18.0000001.71146050.165754yesyes1.8805341.411685Sometimesno1.910378no0.8660451.673584noPublic_TransportationInsufficient_Weight
33Female20.9527371.710730131.274851yesyes3.0000003.000000Sometimesno1.674061no1.4678630.780199SometimesPublic_TransportationObesity_Type_III
44Male31.6410811.91418693.798055yesyes2.6796641.971472Sometimesno1.979848no1.9679730.931721SometimesPublic_TransportationOverweight_Level_II
55Male18.1282491.74852451.552595yesyes2.9197513.000000Sometimesno2.137550no1.9300331.000000SometimesPublic_TransportationInsufficient_Weight
66Male29.8830211.754711112.725005yesyes1.9912403.000000Sometimesno2.000000no0.0000000.696948SometimesAutomobileObesity_Type_II
77Male29.8914731.750150118.206565yesyes1.3974683.000000Sometimesno2.000000no0.5986550.000000SometimesAutomobileObesity_Type_II
88Male17.0000001.70000070.000000noyes2.0000003.000000Sometimesno3.000000yes1.0000001.000000noPublic_TransportationOverweight_Level_I
99Female26.0000001.638836111.275646yesyes3.0000003.000000Sometimesno2.632253no0.0000000.218645SometimesPublic_TransportationObesity_Type_III
idGenderAgeHeightWeightfamily_history_with_overweightFAVCFCVCNCPCAECSMOKECH2OSCCFAFTUECALCMTRANSWeightCategory
1552315523Male19.0000001.80000085.000000yesno3.0000003.000000Sometimesno3.000000no3.0000000.000000SometimesWalkingOverweight_Level_I
1552415524Male24.0000001.70000062.000000yesno2.0000003.000000Frequentlyno2.000000no0.0000001.000000SometimesPublic_TransportationNormal_Weight
1552515525Female21.0000001.56000050.000000noyes2.0000003.000000Sometimesno1.000000no0.0000001.000000noPublic_TransportationNormal_Weight
1552615526Male21.0000001.72000066.000000noyes2.0000003.000000Sometimesno2.000000no1.0000001.000000noPublic_TransportationNormal_Weight
1552715527Female19.9112461.53264342.000000noyes2.7464083.994588Frequentlyno1.000000no2.0000000.000000SometimesPublic_TransportationInsufficient_Weight
1552815528Male18.0000001.70000050.000000noyes2.0000003.000000Frequentlyno2.000000no1.0000002.000000SometimesPublic_TransportationInsufficient_Weight
1552915529Male18.0000001.76310155.523481yesyes2.7860083.000000Sometimesno1.962646yes0.0282021.561272SometimesPublic_TransportationInsufficient_Weight
1553015530Female19.0102111.68693649.660995noyes1.0535343.452590Sometimesno1.000000no2.0012301.000000SometimesPublic_TransportationInsufficient_Weight
1553115531Male22.7778901.80544585.228116yesyes2.0000002.092179Sometimesno2.452986no0.7967700.000000SometimesPublic_TransportationOverweight_Level_I
1553215532Male39.3715231.77027879.677930yesyes2.4078171.097312Sometimesno2.205911no0.9779290.000000FrequentlyAutomobileOverweight_Level_II